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14th International Conference on Computer and Knowledge Engineering
Deep Learning-Based Malaysian Sign Language (MSL) Recognition: Exploring the Impact of Color Spaces
Authors :
Ervin Gubin Moung
1
Precilla Fiona Suwek
2
Maisarah Mohd Sufian
3
Valentino Liaw
4
Ali Farzamnia
5
Wei Leong Khong
6
1- Faculty of Computing and Informatics University Malaysia Sabah
2- Faculty of Computing and Informatics University Malaysia Sabah
3- Faculty of Computing and Informatics Universiti Malaysia Sabah
4- Faculty of Computing and Informatics Universiti Malaysia Sabah
5- School of Computing and Engineering University of Huddersfield
6- School of Engineering Monash University Malaysia
Keywords :
sign language،Malaysian Sign Language،color space،ResNet18،Convolutional Neural Network (CNN)
Abstract :
Sign Language is one form of communication for this group of people to communicate with each other. Not only for people with hearing problems but sign language is also useful for people who are mute or have problem speaking. The most used sign language is the American Sign Language (ASL) that is widely used in English speaking countries. In Malaysia, Bahasa Isyarat Malaysia (BIM) or Malaysian Sign Language (MSL) is still new to the community in Malaysia. In this project, a dataset with 5980 images of the signed alphabet is used to train models to recognize what the signs mean. The problem this project aims to address is the limited research and available datasets in the field of Malaysian Sign Language (MSL) recognition using deep learning and various color spaces. Two models that are used are Convolutional Neural Network (CNN) and Residual Network 18 (ResNet18). The images are also converted into different color spaces which are RGB, YCbCr, Grayscale and the combination of RGB and YCbCr. From the results, RGB is the best color space with CNN without any image processing technique - 80% testing accuracy, with Histogram Equalization (HE) - 82.40% testing accuracy, and with Contrast Limited Adaptive HE (CLAHE) - 83.90%. Whereas YCbCr is the best color space when using ResNet18 without any image processing technique - 88% testing accuracy, with HE - 84.40% testing accuracy, and with CLAHE - 88.30%. The precision, recall, and F1-score metrics are also have been used to evaluate the efficacy of the suggested system.
Papers List
List of archived papers
Deep Learning Feature Extraction for COVID-19 Detection Algorithm using Computerized Tomography Scan
Maisarah Mohd Sufian - Ervin Gubin Moung - Chong Joon Hou - Ali Farzamnia
A New Application of Machine Learning Based Methods for Disk Space Variation Fault Diagnosis in Transformer Windings
Reza Behkam - Amir Lotfi - Gevork B. Gharehpetian
Improvement of Credit Scoring by LSTM Autoencoder Model
Milad Sattari Maleki - Seyedeh Niusha Motevallian - Faezehsadat Hosseini - Mohammad Sabokrou - Hamidreza Soltanalizadeh Maleki
Pruning and Mixed Precision Techniques for Accelerating Neural Network
Mahsa Zahedi - Mohammad Sediq Abazari Bozhgani - Abdorreza Savadi
A Deep CNN Model Based Ensemble Approach for Semantic and Instance Segmentation of Indoor Environment
Sajad Rezaei - Jafar Tanha - Zahra Jafari - SeyedEhsan Roshan - Mohammad-Amin Memar Kochebagh
Classification of COVID-19 and Nodule in CT Images using Deep Convolutional Neural Network
Amirhossein Ghaemi - Seyyed Amir Mousavi mobarakeh - Habibollah Danyali - Kamran Kazemi
SAT Based Analogy Evaluation Framework For Persian Word Embeddings
Seyed Ehsan Mahmoudi - Mehrnoush Shamsfard
Automatic Detection and Risk Assessment of Session Management Vulnerabilities in Web Applications
Nasrin Garmabi - Mohammad Ali Hadavi
Density Estimation Helps Adversarial Robustness
Afsaneh Hasanebrahimi - Bahareh Kaviani Baghbaderani - Reshad Hosseini - Ahmad Kalhor
A Robust Network for Embedded Traffic Sign Recognation.
Omid Nejati Manzari - Shahriar Baradaran Shokouhi
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